/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    InfoGainAttributeEval.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.attributeSelection;

import java.util.Enumeration;
import java.util.Vector;

import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.ContingencyTables;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.supervised.attribute.Discretize;
import weka.filters.unsupervised.attribute.NumericToBinary;

/**
 * <!-- globalinfo-start --> InfoGainAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute by measuring the information gain with
 * respect to the class.<br/>
 * <br/>
 * InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).<br/>
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -M
 *  treat missing values as a seperate value.
 * </pre>
 * 
 * <pre>
 * -B
 *  just binarize numeric attributes instead 
 *  of properly discretizing them.
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Mark Hall (mhall@cs.waikato.ac.nz)
 * @version $Revision$
 * @see Discretize
 * @see NumericToBinary
 */
public class InfoGainAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler {

    /** for serialization */
    static final long serialVersionUID = -1949849512589218930L;

    /** Treat missing values as a seperate value */
    private boolean m_missing_merge;

    /** Just binarize numeric attributes */
    private boolean m_Binarize;

    /** The info gain for each attribute */
    private double[] m_InfoGains;

    /**
     * Returns a string describing this attribute evaluator
     * 
     * @return a description of the evaluator suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
        return "InfoGainAttributeEval :\n\nEvaluates the worth of an attribute " + "by measuring the information gain with respect to the class.\n\n" + "InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).\n";
    }

    /**
     * Constructor
     */
    public InfoGainAttributeEval() {
        resetOptions();
    }

    /**
     * Returns an enumeration describing the available options.
     * 
     * @return an enumeration of all the available options.
     **/
    @Override
    public Enumeration<Option> listOptions() {
        Vector<Option> newVector = new Vector<Option>(2);
        newVector.addElement(new Option("\ttreat missing values as a seperate " + "value.", "M", 0, "-M"));
        newVector.addElement(new Option("\tjust binarize numeric attributes instead \n" + "\tof properly discretizing them.", "B", 0, "-B"));
        return newVector.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     * -M
     *  treat missing values as a seperate value.
     * </pre>
     * 
     * <pre>
     * -B
     *  just binarize numeric attributes instead 
     *  of properly discretizing them.
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {

        resetOptions();
        setMissingMerge(!(Utils.getFlag('M', options)));
        setBinarizeNumericAttributes(Utils.getFlag('B', options));
    }

    /**
     * Gets the current settings of WrapperSubsetEval.
     * 
     * @return an array of strings suitable for passing to setOptions()
     */
    @Override
    public String[] getOptions() {
        String[] options = new String[2];
        int current = 0;

        if (!getMissingMerge()) {
            options[current++] = "-M";
        }
        if (getBinarizeNumericAttributes()) {
            options[current++] = "-B";
        }

        while (current < options.length) {
            options[current++] = "";
        }

        return options;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String binarizeNumericAttributesTipText() {
        return "Just binarize numeric attributes instead of properly discretizing them.";
    }

    /**
     * Binarize numeric attributes.
     * 
     * @param b true=binarize numeric attributes
     */
    public void setBinarizeNumericAttributes(boolean b) {
        m_Binarize = b;
    }

    /**
     * get whether numeric attributes are just being binarized.
     * 
     * @return true if missing values are being distributed.
     */
    public boolean getBinarizeNumericAttributes() {
        return m_Binarize;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String missingMergeTipText() {
        return "Distribute counts for missing values. Counts are distributed " + "across other values in proportion to their frequency. Otherwise, " + "missing is treated as a separate value.";
    }

    /**
     * distribute the counts for missing values across observed values
     * 
     * @param b true=distribute missing values.
     */
    public void setMissingMerge(boolean b) {
        m_missing_merge = b;
    }

    /**
     * get whether missing values are being distributed or not
     * 
     * @return true if missing values are being distributed.
     */
    public boolean getMissingMerge() {
        return m_missing_merge;
    }

    /**
     * Returns the capabilities of this evaluator.
     * 
     * @return the capabilities of this evaluator
     * @see Capabilities
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result = super.getCapabilities();
        result.disableAll();

        // attributes
        result.enable(Capability.NOMINAL_ATTRIBUTES);
        result.enable(Capability.NUMERIC_ATTRIBUTES);
        result.enable(Capability.DATE_ATTRIBUTES);
        result.enable(Capability.MISSING_VALUES);

        // class
        result.enable(Capability.NOMINAL_CLASS);
        result.enable(Capability.MISSING_CLASS_VALUES);

        return result;
    }

    /**
     * Initializes an information gain attribute evaluator. Discretizes all
     * attributes that are numeric.
     * 
     * @param data set of instances serving as training data
     * @throws Exception if the evaluator has not been generated successfully
     */
    @Override
    public void buildEvaluator(Instances data) throws Exception {

        // can evaluator handle data?
        getCapabilities().testWithFail(data);

        int classIndex = data.classIndex();
        int numInstances = data.numInstances();

        if (!m_Binarize) {
            Discretize disTransform = new Discretize();
            disTransform.setUseBetterEncoding(true);
            disTransform.setInputFormat(data);
            data = Filter.useFilter(data, disTransform);
        } else {
            NumericToBinary binTransform = new NumericToBinary();
            binTransform.setInputFormat(data);
            data = Filter.useFilter(data, binTransform);
        }
        int numClasses = data.attribute(classIndex).numValues();

        // Reserve space and initialize counters
        double[][][] counts = new double[data.numAttributes()][][];
        for (int k = 0; k < data.numAttributes(); k++) {
            if (k != classIndex) {
                int numValues = data.attribute(k).numValues();
                counts[k] = new double[numValues + 1][numClasses + 1];
            }
        }

        // Initialize counters
        double[] temp = new double[numClasses + 1];
        for (int k = 0; k < numInstances; k++) {
            Instance inst = data.instance(k);
            if (inst.classIsMissing()) {
                temp[numClasses] += inst.weight();
            } else {
                temp[(int) inst.classValue()] += inst.weight();
            }
        }
        for (int k = 0; k < counts.length; k++) {
            if (k != classIndex) {
                for (int i = 0; i < temp.length; i++) {
                    counts[k][0][i] = temp[i];
                }
            }
        }

        // Get counts
        for (int k = 0; k < numInstances; k++) {
            Instance inst = data.instance(k);
            for (int i = 0; i < inst.numValues(); i++) {
                if (inst.index(i) != classIndex) {
                    if (inst.isMissingSparse(i) || inst.classIsMissing()) {
                        if (!inst.isMissingSparse(i)) {
                            counts[inst.index(i)][(int) inst.valueSparse(i)][numClasses] += inst.weight();
                            counts[inst.index(i)][0][numClasses] -= inst.weight();
                        } else if (!inst.classIsMissing()) {
                            counts[inst.index(i)][data.attribute(inst.index(i)).numValues()][(int) inst.classValue()] += inst.weight();
                            counts[inst.index(i)][0][(int) inst.classValue()] -= inst.weight();
                        } else {
                            counts[inst.index(i)][data.attribute(inst.index(i)).numValues()][numClasses] += inst.weight();
                            counts[inst.index(i)][0][numClasses] -= inst.weight();
                        }
                    } else {
                        counts[inst.index(i)][(int) inst.valueSparse(i)][(int) inst.classValue()] += inst.weight();
                        counts[inst.index(i)][0][(int) inst.classValue()] -= inst.weight();
                    }
                }
            }
        }

        // distribute missing counts if required
        if (m_missing_merge) {

            for (int k = 0; k < data.numAttributes(); k++) {
                if (k != classIndex) {
                    int numValues = data.attribute(k).numValues();

                    // Compute marginals
                    double[] rowSums = new double[numValues];
                    double[] columnSums = new double[numClasses];
                    double sum = 0;
                    for (int i = 0; i < numValues; i++) {
                        for (int j = 0; j < numClasses; j++) {
                            rowSums[i] += counts[k][i][j];
                            columnSums[j] += counts[k][i][j];
                        }
                        sum += rowSums[i];
                    }

                    if (Utils.gr(sum, 0)) {
                        double[][] additions = new double[numValues][numClasses];

                        // Compute what needs to be added to each row
                        for (int i = 0; i < numValues; i++) {
                            for (int j = 0; j < numClasses; j++) {
                                additions[i][j] = (rowSums[i] / sum) * counts[k][numValues][j];
                            }
                        }

                        // Compute what needs to be added to each column
                        for (int i = 0; i < numClasses; i++) {
                            for (int j = 0; j < numValues; j++) {
                                additions[j][i] += (columnSums[i] / sum) * counts[k][j][numClasses];
                            }
                        }

                        // Compute what needs to be added to each cell
                        for (int i = 0; i < numClasses; i++) {
                            for (int j = 0; j < numValues; j++) {
                                additions[j][i] += (counts[k][j][i] / sum) * counts[k][numValues][numClasses];
                            }
                        }

                        // Make new contingency table
                        double[][] newTable = new double[numValues][numClasses];
                        for (int i = 0; i < numValues; i++) {
                            for (int j = 0; j < numClasses; j++) {
                                newTable[i][j] = counts[k][i][j] + additions[i][j];
                            }
                        }
                        counts[k] = newTable;
                    }
                }
            }
        }

        // Compute info gains
        m_InfoGains = new double[data.numAttributes()];
        for (int i = 0; i < data.numAttributes(); i++) {
            if (i != classIndex) {
                m_InfoGains[i] = (ContingencyTables.entropyOverColumns(counts[i]) - ContingencyTables.entropyConditionedOnRows(counts[i]));
            }
        }
    }

    /**
     * Reset options to their default values
     */
    protected void resetOptions() {
        m_InfoGains = null;
        m_missing_merge = true;
        m_Binarize = false;
    }

    /**
     * evaluates an individual attribute by measuring the amount of information
     * gained about the class given the attribute.
     * 
     * @param attribute the index of the attribute to be evaluated
     * @return the info gain
     * @throws Exception if the attribute could not be evaluated
     */
    @Override
    public double evaluateAttribute(int attribute) throws Exception {

        return m_InfoGains[attribute];
    }

    /**
     * Describe the attribute evaluator
     * 
     * @return a description of the attribute evaluator as a string
     */
    @Override
    public String toString() {
        StringBuffer text = new StringBuffer();

        if (m_InfoGains == null) {
            text.append("Information Gain attribute evaluator has not been built");
        } else {
            text.append("\tInformation Gain Ranking Filter");
            if (!m_missing_merge) {
                text.append("\n\tMissing values treated as seperate");
            }
            if (m_Binarize) {
                text.append("\n\tNumeric attributes are just binarized");
            }
        }

        text.append("\n");
        return text.toString();
    }

    // ============
    // Test method.
    // ============
    /**
     * Main method for testing this class.
     * 
     * @param args the options
     */
    public static void main(String[] args) {
        runEvaluator(new InfoGainAttributeEval(), args);
    }
}
